Time-of-flight positron emission tomography reconstruction using image content generated event-by-event based on time-of-flight information

ABSTRACT

A method of processing a positron emission tomography (PET) imaging data set ( 30 ) acquired of a subject includes independently localizing each positron-electron annihilation event of the PET imaging data set based on time of flight (TOF) localization of the positron-electron annihilation event to form a generated image ( 34 ). The generated image may be displayed. The generated image is suitably used as the basis for an initial image of an iterative reconstruction ( 40 ) of the PET imaging data set ( 30 ) to produce a reconstructed image ( 42 ). A spatial contour ( 56 ) of an image of the subject in the PET imaging data set ( 30 ) is suitably delineated based on the generated image ( 34 ). A subject attenuation map ( 62 ) for use in PET image reconstruction ( 40 ) is suitably constructed based in part on the spatial contour ( 56 ).

CROSS REFERENCE TO RELATED APPLICATIONS

This application claims the benefit of U.S. provisional application Ser.No. 61/184,877 filed Jun. 8, 2009 and U.S. provisional application Ser.No. 61/225,580 filed Jul. 15, 2009, both of which are incorporatedherein by reference.

The following relates to the imaging arts, positron emission tomography(PET) imaging arts, time-of-flight (TOF) PET imaging arts, medicalimaging arts, and related arts.

Positron emission tomography (PET) imaging entails administering aradiopharmaceutical including a positron-emitting radioisotope to asubject, and detecting 511 keV gamma rays generated by positron-electronannihilation events. Momentum and energy conservation causes eachpositron-electron annihilation event to emit two oppositely directed 511keV gamma rays—accordingly, two substantially simultaneous 511 keV gammaray detection events correspond to a single detected positron-electronannihilation event. Absent scattering, the detected positron-electronannihilation event lies somewhere on the straight line connecting thetwo 511 keV gamma ray detection events.

A PET imaging data set comprises a set of such detectedelectron-positron annihilation events, which can be reconstructed intoan image using a suitable reconstruction algorithm. The reconstructedimage represents a spatial distribution of the positron-electronannihilation events, which effectively corresponds to a spatialdistribution of the radiopharmaceutical in the subject due to a shortmean positron travel distance before annihilation. Theradiopharmaceutical can be selected to accumulate in an organ or tissueof interest, such as the liver or brain, so as to provide a clinicallyuseful image for medical or veterinary purposes. If theradiopharmaceutical is a substance that incorporates into a metabolicpathway, the PET image can provide functional information about themetabolic pathway. Some known PET image reconstruction algorithmsinclude filtered backprojection and iterative backprojection. The lattertechnique is robust against noise, and is well-suited for reconstructingimaging data in the presence of noise.

An improvement to conventional PET imaging is time-of-flight (TOF) PETimaging. TOF-PET further localizes the positron-electron annihilationevent along the straight line connecting the two 511 keV gamma raydetection events based on a time difference (or lack thereof) betweenthe two “substantially simultaneous” 511 keV gamma ray detection events.Intuitively, this can be seen as follows. If one detection event isearlier than the other, then the positron-electron annihilation event islikely to have occurred at a point along the connecting lineproportionately closer to the earlier detection event. On the otherhand, if the two detection events are perfectly simultaneous, then thepositron-electron annihilation event is likely to have occurred at apoint about midway along the connecting line. In practice, the TOFlocalization is limited by the temporal resolution of the gamma raydetectors, and can be represented as a TOF “kernel” indicative of apeaked probability density function along the connecting line.

PET image reconstruction by iterative backprojection is computationallyintensive. It can take several minutes or longer to reconstruct an imagewith clinically useful accuracy and resolution for medical or veterinaryapplication. Utilizing TOF localization in the image reconstruction addsfurther computational complexity and results in still longer iterativereconstruction time. Until the iterative reconstruction is complete, itis generally not known whether the final image will be of satisfactoryclinical quality. The subject (e.g., a medical or veterinary patient) istypically kept in the PET scanner until the image reconstruction iscomplete and the clinician visually verifies that the finalreconstructed image is of satisfactory quality, so that additionalimaging data can be acquired if the final reconstructed image turns outto be unsatisfactory. This is unpleasant for the subject, and alsoreduces PET imaging facility throughput.

Another problem with existing PET imaging is gamma ray attenuation bythe subject. In general, the regions of accumulated radiopharmaceuticalappear as bright regions in the PET image (assuming a positive image)while regions that remain relatively free of the radiopharmaceuticalappear as darker regions. Regions with no radiopharmaceutical at all,such as the air surrounding the subject, are completely dark orinvisible (neglecting any noise or image artifacts). The dark regions ofthe subject are not irrelevant, however—they absorb some gamma rayparticles and hence reduce the 511 keV counts in a generally spatiallyvarying manner. To compensate, it is known to employ a subjectattenuation map during the iterative reconstruction to account for thespatially varying attenuation of 511 keV gamma radiation.

In some existing systems, the PET scanner is combined with atransmission computed tomography (CT) scanner, and the latter is used toacquire radiation attenuation data that can be used to generate thesubject attenuation map. Although the radiation used in the CT imagingis typically not at 511 keV, suitable compensation for the difference inradiation energy is known and readily performed.

However, the CT scanner may be unavailable, or may have a smaller fieldof view (FOV) than the PET scanner. For example, some imaging systemscombine a PET scanner with a magnetic resonance (MR) scanner whichtypically has a substantially smaller FOV than the PET scanner. In suchcases, a complete subject attenuation map cannot be generated directlyfrom the CT or MR image.

The following provides new and improved apparatuses and methods whichovercome the above-referenced problems and others.

In accordance with one disclosed aspect, a method of processing apositron emission tomography (PET) imaging data set acquired of asubject comprises generating image content independently for eachpositron-electron annihilation event of a plurality of positron-electronannihilation events of the PET imaging data set based on time of flight(TOF) localization to form a generated image comprising an accumulationof the independently generated image content, wherein the generatingoperations are performed by a digital processor.

In accordance with another disclosed aspect, a method of processing apositron emission tomography (PET) imaging data set acquired of asubject comprises independently localizing each positron-electronannihilation event of the PET imaging data set based on time of flight(TOF) localization of the positron-electron annihilation event to form agenerated image, wherein the independent localizing operations areperformed by a digital processor.

In accordance with another disclosed aspect, a method of PET imagingcomprises determining a likely location of a detected positron-electronannihilation event based on time of flight (TOF) information, repeatingthe determining for a plurality of detected positron-electronannihilation events to generate a scout image, and displaying the scoutimage.

In accordance with another disclosed aspect, a disclosed digitalprocessor is configured to perform a method as set forth in any one ofthe three immediately preceding paragraphs. In accordance with anotherdisclosed aspect, a disclosed storage medium stores instructionsexecutable by a digital processor to perform a method as set forth inany one of the three immediately preceding paragraphs.

One advantage resides in providing faster iterative PET imagereconstruction.

Another advantage resides in providing more accurate PET imagereconstruction.

Another advantage resides in providing a more accurate and completesubject attenuation map for use in PET image reconstruction.

Another advantage resides in providing fast generation of scout imagesfor monitoring or planning a clinical PET imaging acquisition.

Further advantages will be apparent to those of ordinary skill in theart upon reading and understand the following detailed description.

FIG. 1 diagrammatically shows a positron emission tomography (PET)imaging system using image content generated event-by-event based ontime-of-flight (TOF) information.

FIGS. 2 and 3 diagrammatically show alternative approaches forgenerating image content event-by-event based on time-of-flight (TOF)information.

FIGS. 4 and 5 diagrammatically flowchart scout image generation andprocessing methods suitably performed by the PET imaging system of FIG.1.

With reference to FIG. 1, an imaging system 10 includes at leasttime-of-flight (TOF) positron emission tomography (PET) imagingcapability. Toward this end, the imaging system 10 includes a subjectsupport 12 for loading an imaging subject into the bore of a gantry 14which houses one or more rings of PET detectors (not shown) capable ofdetecting 511 keV gamma rays. Optionally, the imaging system 10 may becapable of performing at least one additional imaging modality otherthan PET, such as being capable of performing magnetic resonance (MR)imaging or transmission computed tomography (CT) imaging. For example,the gantry 14 may further house a magnet, magnetic field gradient coils,and other components for MR imaging (not shown). Alternatively, theimaging system 10 may include an additional gantry (not shown) with abore arranged coaxially with the bore of the illustrated gantry 14 sothat the subject support 12 can load the subject into either the gantry14 or into the additional gantry. The additional gantry can housecomponents implementing another imaging modality such as MR or CT. Somesuitable imaging systems include, for example, the Gemini™ series ofTOF-PET/CT imaging systems that provide TOF-PET and CT imagingcapabilities in separate PET and CT gantries with coaxial bores alignedto operate with a common subject support (available from KoninklijkePhilips Electronics N.V., Eindhoven, the Netherlands).

The imaging system 10 is controlled by an imaging system controller 16that interfaces with a human user (for example, a radiologist, medicaldoctor, veterinarian, or so forth) via an illustrated computer 18 havinga display 20 and one or more user input devices 22, or via anothersuitable user interface. The imaging system controller 16 and userinterface 18 can be variously embodied—for example, the imaging systemcontroller 16 may be embodied as a digital processor such as thecomputer 18 running suitable imaging system control software, or mayadditionally or alternatively include specialized imaging system controlhardware such as one or more dedicated imaging system control digitalprocessors, or so forth. The illustrated user input device 22 is akeyboard, but more generally one or more user interface devices can beprovided, such as for example any combination of a keyboard, mouse,trackball, touch screen, or so forth. The display provides feedback tothe user from the imaging system 10, and also displays various acquiredimages. Although a single display 20 is illustrated, it is contemplatedto have two or more displays, such as for example a graphical displayfor showing acquired images and one or more text-based or lowerresolution graphical displays (such as, for example, an LCD screen) fordisplaying interfacing textual messages, low resolution bar indicatorsor other low resolution graphical indicators, or so forth.

The imaging system 10 acquires a TOF-PET imaging data set 30 undercontrol of the controller 16 and with optional user control input viathe user interface 18. Toward this end, a subject (such as a humanimaging subject, an animal veterinary subject, or so forth) isadministered a radiopharmaceutical including a radioisotope that emitspositrons, and the subject is loaded into the imaging system 10 for PETimaging. The radiopharmaceutical can be administered either before orafter loading the subject into the imaging system 10, but should beadministered a sufficient time prior to acquiring the TOF-PET imagingdata set 30 so that the radiopharmaceutical can accumulate or aggregatein an organ or tissue of interest, or can metabolize via a metabolicpathway of interest, or can otherwise interact with or distributethrough the subject in a manner such that the acquired TOF-PET imagingdata set 30 contains useful information. For example, in the case ofbrain imaging sufficient time should be provided to allow theradiopharmaceutical to accumulate in brain tissue of interest.

In the case of a living human or animal subject, the dosage ofradiopharmaceutical is preferably limited as dictated by acceptedmedical standards, veterinary standards, applicable governmentregulations, facility guidelines, individual medical judgment, or soforth. The dosage is typically relatively low, and as a consequence ittypically takes several seconds, tens of seconds, several minutes, orlonger in order to detect a sufficient number of positron-electronannihilation events to form a clinically useful image. Further,reconstruction of the TOF-PET imaging data set 30 is computationallyintensive and can take several seconds, tens of seconds, severalminutes, or longer. As a consequence, the time from initiating imageacquisition to display of a reconstructed image can be lengthy.

To address these problems and provide more rapid visual feedback to theuser, an image generator 32 is operate to generate image contentindependently for each positron-electron (p-e) annihilation event basedon time of flight localization, so as to form a generated image 34comprising an accumulation of the independently generated image content.The image generator 32 generates image content for each p-e annihilationevent independently, and accordingly can begin generating the imagecontent of the generated image 34 as soon as the first p-e annihilationevent is recorded in the memory storing the TOF-PET imaging data set 30.A generated image scout navigator 36 suitably displays the generatedimage 34 on the display 22 for review by the user. Optionally, thegenerated image scout navigator 36 can display the image content of thegenerated image 34 in real time as it is generated by the imagegenerator 32. The effect is to show a gradual “filling in” of thedisplayed image as progressively more image content is generated. Thus,the user receives nearly instantaneous visual feedback and can watch asthe generated image is formed over time as progressively more imagecontent is added as the number of detected e-h annihilation eventsincreases over time.

With brief reference to FIGS. 2 and 3, operation of the image generator32 is further described. In each of FIG. 2 and FIG. 3, a portion of animage space S is shown diagrammatically, with pixels (or, more generallyfor three dimensions, voxels) indicated by a grid of boxes. Each boxrepresents a pixel or voxel. Although the illustrated pixels or voxelsare square, rectangular or otherwise-shaped pixels or voxels are alsocontemplated. In FIG. 2, a first illustrative detected e-h annihilationevent is defined by two substantially simultaneous 511 keV gamma raydetection events connected by a connecting line or “line of response”denoted in FIG. 2 as LOR1. The time-of-flight information for the firstdetected e-h annihilation event is represented as a probability densityfunction or kernel diagrammatically depicted in FIG. 2 as a plotrespective to the line of response LOR1 and denoted TOF1. In similarfashion, a second illustrative detected e-h annihilation event isindicated by a line-of-response LOR2 and time-of-flight kernel TOF2, anda third illustrative detected e-h annihilation event is indicated by aline-of-response LOR3 and time-of-flight kernel TOF3. In FIG. 3, asingle illustrative detected line-of-response LOR4 is depicted withcorresponding TOF kernel TOF4.

The TOF kernels TOF1, TOF2, TOF3, TOF4 are peaked distributions whosepeak locations along the respective lines-of-response LOR1, LOR2, LOR3,LOR4 are determined by the time difference between the two substantiallysimultaneous 511 keV gamma ray detection events that define the detectede-h annihilation event. The widths of TOF kernels TOF1, TOF2, TOF3, TOF4are determined by the temporal resolution of the PET detectors.

In the embodiment of FIG. 2, the image generator 32 operates byincreasing a value of an image pixel or voxel of the image space S atthe peak of the TOF localization by a selected amount such that theaccumulation of value increases forms the generated image 34. The peakof the TOF localization corresponds to the peak of the time-of-flightkernel representation, that is, the most probable location of thedetected e-h annihilation event, and can be computed based on theline-of-response and the time difference between the two substantiallysimultaneous 511 keV gamma ray detection events that define the detectede-h annihilation event. For example, in illustrative FIG. 2 an e-hannihilation event represented by line-of-response LOR1 and TOF kernelTOF1 is processed to generate image content by increasing a value of animage pixel or voxel P1 of the image space S that most probably containsthe e-h annihilation event. This is indicated in FIG. 2 by a lightshading of pixel or voxel P1. On the other hand, the peaks of thetime-of-flight kernels TOF2, TOF3 along respective lines-of-responseLOR2, LOR3 happen to coincide at a same pixel or voxel P2. The value ofpixel or voxel P2 is therefore increased twice, as indicated by a darkershading of pixel or voxel P2 in FIG. 2. Although not illustrated, itwill be appreciated that a third or more e-h annihilation events whoseTOF kernels happen to peak at the same pixel or voxel would result infurther increases in the value of that pixel or voxel. Cumulatively, thevalue of the pixel or voxel is indicative of the detected e-hannihilation activity in that pixel or voxel of the space S, and hencethe resulting accumulated image content (corresponding to increases inthe value of a pixel or voxel value made on a per e-h annihilation eventbasis) cumulatively define an image of the e-h annihilation activity inthe image space S, which in turn is an image representation of thedistribution of radiopharmaceutical in the subject.

The resolution of the generated image 34 when generated using theapproach of FIG. 2 is limited by the widths of the TOF kernels, that is,by the spatial uncertainty introduced by the finite temporal resolutionof the TOF information due to the finite temporal resolution of the PETdetectors. The speed of light in a vacuum is 3.00×10¹⁰ cm/sec. If thePET detectors have a temporal resolution of 300 picosecond, then theTOF-limited spatial resolution is on the order of 10 cm. The resolutioncould be worse than this, for example if there is motion blurring,distance blurring, or so forth. Thus, for PET detectors with 300picosecond temporal resolution, the generated image 34 when used as ascout image is not of clinical quality—however, if a low concentrationof the radiopharmaceutical permeates the entire volume of the subject,then the generated image 34 provides a reasonably accurate outline ofthe subject, with brighter areas being indicative of areas of moreconcentrated radiopharmaceutical accumulation. The generated image 34can have resolution of clinical quality if the PET detectors are ofsuitably low temporal resolution, e.g. on the order of a few tens ofpicoseconds, and more preferably on the order of a few picoseconds orshorter.

The approach of forming the generated image 34 described with referenceto FIG. 2 does not take into account the width or shape of the TOFkernels (although the spatial resolution may be limited by the width orshape of the TOF kernels).

With reference to FIG. 3, in an alternative approach the image generator34 generates the image content for each e-h annihilation event byincreasing values of image pixels or voxels of the image space S byamounts corresponding to the TOF kernel representing the TOFlocalization such that the accumulation of value increases forms thegenerated image. In the example illustrated in FIG. 3, the width of theTOF kernel TOF4 along the line-of-response LOR4 extends over four pixelsor voxels denoted P10, P11, P12, P13. The peak of the TOF kernel TOF4approximately coincides with the pixel or voxel P12 which is assignedthe highest value increment of 5. (Note that in FIG. 3 the valueincrements are indicated numerically, rather than by shading as in FIG.2). The pixel or voxel P11 is at a lower part of the TOF kernel TOF4 andis assigned a correspondingly lower value increment of 3. The remainingpixels or voxels P10, P13 are assigned respective value increments 1 and2, respectively, in accordance with the values of the TOF kernel TOF4coinciding with these pixels or voxels. Thus, the image contentcorresponding to the e-h annihilation event is an intensity distributioncontribution in the approach of FIG. 3, specifically an intensitydistribution contribution over the pixels or voxels P10, P11, P12, P13for the illustrative e-h annihilation event diagrammatically depicted inFIG. 3. As with the approach of FIG. 2, these pixel or voxel valueincreases are performed on a per e-h annihilation event basis, and theimage content comprising these value increases are accumulated to formthe generated image 34.

The approach of FIG. 3 more accurately accounts for the spread oruncertainty of the TOF information by modeling the shape and width ofthe TOF kernel. It is to be appreciated that the TOF kernel width andshape can take into account other sources of resolution degradationbesides PET detector temporal resolution, such as distance blurringcaused by finite spatial resolution of the PET detectors. Additionally,the TOF kernel width and/or shape can vary across the image space S, forexample based on the distance between the peak of the TOF kernel and theproximate PET detector.

With returning reference to FIG. 1, the generated image 34 can be usedin various ways to substantially improve the TOF-PET imaging. As alreadydescribed, the generated image 34 can be used by the generated imagescout navigator 36 to provide immediate feedback to the user regardingprogress of the PET imaging data acquisition. Depending upon what theuser sees in the generated image 34, the user may elect to repositionthe subject or make additional or other adjustments in the PET dataacquisition. If the scout navigator 36 displays the generated image 34as it is being generated (that is, as progressively more e-hannihilation events are detected and corresponding image contentgenerated on a per e-h annihilation event basis) then the user may beable to identify certain coarse image problems (such as a large error insubject positioning or a substantial failure of the radiopharmaceuticalto accumulate in the organ or tissue of interest, perhaps due to anincorrect choice of radiopharmaceutical) early in the imaging, and cantake corrective action or abort the imaging session altogether based onthis early information.

The generated image 34 can be used for other purposes. For example, insome embodiments an iterative PET image reconstruction engine 40performs an iterative reconstruction of the PET imaging data set 30 toproduce a reconstructed image 42 that is displayed on the display 20 orotherwise utilized. In general, an iterative reconstruction operates bystarting with an initial image, simulating PET imaging data expected tobe generated if a subject corresponding to the initial image were to beimaged by a PET scanner, and iteratively adjusts the initial image andrepeats the simulation until the simulated PET imaging datasubstantially comports with the PET imaging data set 30. The speed andaccuracy of iterative reconstruction depends to a substantial extent onhow close the initial image is to the final reconstructed image. In theembodiment of FIG. 1, the iterative reconstruction uses the generatedimage 34 as the initial image for the iterative reconstruction. Thegenerated image 34 is expected to be the same as the final reconstructedimage except that the generated image 34 is typically of a substantiallymore coarse resolution due to its being limited by the TOF spatialresolution. Accordingly, the generated image 34 is a suitable choice asthe initial image for the iterative reconstruction. Optionally, theinitial image may be derived from the generated image 34 by suitableprocessing such as smoothing, intensity scaling, or other preprocessing(not illustrated).

With continuing reference to FIG. 1, another suitable use of thegenerated image 34 is in the generation of a subject attenuation map foruse in the iterative image reconstruction 40 (as illustrated) or for usein another type of image reconstruction. This application relies uponthe radiopharmaceutical being present at a detectable concentrationlevel in the most or all of the portion of the subject disposed in thePET imaging volume. For example, a radiopharmaceutical that targetsbrain tissue is expected to accumulate in the brain, but a lowerconcentration of radiopharmaceutical is also expected to be present inthe bloodstream and hence is expected to perfuse into skin, muscle, orother tissues in the head and shoulders portion of the subject that isdisposed in the PET imaging volume (in the case of brain imaging). As aresult, the generated image 34 includes a discernable image of thesubject portion disposed in the PET imaging volume as a whole. A subjectattenuation map generator 50 applies an image smoother 52 and edgedetector 54 or other image segmentation algorithm to identify a spatialcontour of the image of the subject 56. Other processing can be employedto identify the spatial contour 56. Moreover, the relatively coarseresolution imposed by the TOF spatial resolution in the case of some PETdetectors may provide sufficient smoothing so that the image smoother 52may be optionally omitted.

A subject attenuation map construction engine 60 constructs a subjectattenuation map 62 for use in PET image reconstruction based on thesubject spatial contour 56. The attenuation values within the subjectspatial contour 56 can be determined from various sources. In someembodiments, a subject attenuation map 64 is available from a non-PETimaging modality. For example, if the imaging system 10 includes asecond imaging modality such as MR or CT, then this second imagingmodality can be used to acquire the subject attenuation map 64. However,in this case the subject attenuation map 64 may be truncated due to asmaller field of view of the MR or CT imaging modality as compared withthe PET imaging modality of the imaging system 10. The subjectattenuation map construction engine 60 uses the truncated subjectattenuation map 64 as a nucleus, and spatially extends the truncatedsubject attenuation map 64 to fill the larger PET imaging volume basedon a subject model 66, such as a three-dimensional anatomical model of ahuman subject. Thus, for example, if the subject attenuation map 64includes regions of muscle tissue having a certain average attenuationvalue for muscle, this can be spatially extended to other regionsoutside the truncated subject attenuation map 64 which are expected toalso comprise muscle tissue based on the subject model 66.

As another alternative, the subject attenuation map 64 may be acquiredusing a different imaging system, or may be computed from the subjectmodel 66 based on known 511 keV attenuation values for various tissues.In such cases, however, the subject attenuation map 64 may be out ofspatial registration with the subject disposed on the subject support 12for PET imaging. For example, the subject attenuation map 64 may be forthe subject in a different position, or may be for a similar butdifferent subject of different size, or so forth. In such cases, thesubject spatial contour 56 can be used as a spatial reference, and thesubject attenuation map 64 is suitably spatially registered with thesubject spatial contour 56 by a selected rigid or nonrigid spatialregistration algorithm.

The resulting subject attenuation map 62 is suitably used by theiterative PET image reconstruction engine 40 to take into accountabsorption of 511 keV gamma rays by tissues of the subject in performingthe iterative reconstruction. It should be noted that the generatedimage 34 may be used: (1) both in constructing the subject attenuationmap 62 and as the initial image for the reconstruction 40 (as shown); or(2) the generated image 34 may be used only in constructing the subjectattenuation map 62 but not as the initial image for an iterativereconstruction (for example, if a non-iterative image reconstructionalgorithm is employed which does not utilize an initial image); or (3)the generated image 34 may be used only as the initial image for thereconstruction 40 (for example, if a satisfactory subject attenuationmap coextensive with the PET imaging volume is already available from CTimaging or another source).

With reference to FIGS. 4 and 5, these illustrative applications arediagrammatically shown in a flowchart format. As shown in FIG. 4, theTOF-PET imaging data set is processed by the image generator 32 to formthe generated image 34. The image generator 32 operates independently oneach acquired positron-electron annihilation event in order toaccumulate image content. In a suitable process, a decision operation 70detects whether there are TOF-PET imaging data to process. If so, apositron-electron annihilation event is selected for processing inoperation 72, and image content is generated for the selectedpositron-electron annihilation event in an operation 74 based on theline-of-response and the TOF localization. The operation 74 can employthe approach diagrammatically shown in FIG. 2 in which a value of animage pixel or voxel P1, P2 of the image space S at the peak of the TOFlocalization is increased by a selected amount. Alternatively, theoperation 74 can employ the approach diagrammatically shown in FIG. 3 inwhich values of image pixels or voxels P10, P11, P12, P13 of the imagespace S are increased by amounts corresponding to a TOF kernel TOF4representing the TOF localization. In either case, an image contentaccumulator 76 accumulates the value increase or increases to contributeto the generated image 34. Processing returns to the decision operation70 to process the next available acquired positron-electron annihilationevent. If no unprocessed events are available, operation of the imagegenerator 32 ceases or goes into an idle mode in operation 78. Forexample, if the image generator 32 is applied after the PET imaging dataacquisition is complete, then the operation 78 is suitably a stopoperation. On the other hand, if the image generator 32 is appliedcontinuously during the PET imaging data acquisition so as tocontinually add image content to the generated image 34 in substantiallyreal time, then the operation 78 is suitably an idle operation in whichthe image generator 32 waits until the next positron-electronannihilation event datum is acquired.

The resulting generated image 34 suitably serves as a scout image thatcan be displayed, rendered, or otherwise visualized by the generatedimage scout navigator 36. In the case of substantially real-timeoperation the scout image appears to be gradually filled in as imagecontent corresponding to newly acquired positron-electron annihilationevents are added by the image generator 32. In this case, the usersuitably has the option of issuing an image erase operation 80 via thescout navigator 36 to erase the generated image 34 in order to beginaccumulation of image content comprising a new generated image. Forexample, the user may select the image erase operation 80 when thesubject is repositioned such that the scout image no longer correspondsto the current subject position.

FIG. 5 diagrammatically illustrates operation of an embodiment of thesubject attenuation map construction engine 60 of FIG. 1. In thisembodiment, the truncated subject attenuation map 64 is received fromanother imaging modality such as CT or MR, and the subject attenuationmap construction engine 60 spatially extends the truncated map to fillthe PET field-of-view (FOV) based on the spatial contour 56 of the imagesubject which is extracted from the generated scout image 34. Thespatial contour 56 may, for example, be generated by operation of theimage smoother 52 and edge detector 54 illustrated in FIG. 1 as alreadydescribed. If needed, the subject attenuation map construction engine 60uses the truncated subject attenuation map 64 as a nucleus, andspatially extends the truncated subject attenuation map 64 to fill thelarger PET imaging FOV based on the subject model 66. Toward this end,the attenuation map 64 is compared with the subject model 66 to identifyand tabulate attenuation characteristics for the various tissues in anoperation 90. If appropriate, the attenuation map 64 is also spatiallyregistered with the spatial contour 56 in an operation 92. Theattenuation map 64 (after optional spatial registration 92) is thenextended to the PET FOV in an operation 94 based on the spatial contour56 which identifies the extent of the subject outside of the truncatedFOV of the attenuation map 64 and based on identification of tissue ortissues in the extended FOV determined based on the subject model 66 andthe tabulated attenuation characteristics for those tissues asdetermined by the operation 90. For example, if the attenuation map 64includes an elongate bone surrounded by muscle that is truncated at theboundary of the attenuation map 64, the spatial contour 56 and subjectmodel 66 can be used to determine the extended range of bone tissue andof muscle tissue, respectively, spanning the PET FOV, and theattenuation map extended using respective bone and muscle tissueattenuation values as determined in the operation 90. The result is thesubject attenuation map 62 for use in PET image reconstruction.

The illustrative applications of the generated image 34, including useas a scout image, use in constructing a subject attenuation map, and useas an initial image for iterative reconstruction, as described withreference to FIG. 1, are merely illustrative examples. Otherapplications are contemplated for the generated image 34. For example,if the PET detectors provide sufficient TOF spatial resolution for agiven clinical application, the generated image 34 is contemplated foruse as the clinical image employed in medical or veterinary diagnosis orother clinical applications. Similarly, it is contemplated for thegenerated image 34 to be used for certain medical screening procedureswhere the TOF-limited spatial resolution of the generated image 34 maybe sufficient to perform a threshold-based screening. For example, if aradiopharmaceutical is known to accumulate in malignant tumours in agiven tissue but not in the healthy tissue, then screening based onthresholding integrated PET image intensity for regions of the giventissue ratioed respective to integrated PET image intensity for the bodyas a whole may be sufficient to provide screening for certain types ofcancer in the given tissue. Yet another contemplated application of thegenerated image 34 is as a validation check for complex iterativereconstruction algorithms that may have a tendency to converge tononphysical solutions. By comparing the final iteratively reconstructedimage with the generated image 34 (which is likely to be physicallyaccurate albeit at coarse spatial resolution) a nonphysical iterativesolution can be detected and discarded.

Yet another contemplated application is detection of subject motion inapproximately real time. As already noted, the generated image scoutnavigator 36 optionally displays the generated image as the imagecontent is generated over the image acquisition. This continuouslyupdated scout image represents a probability distribution for e-hannihilation events. As the PET data acquisition progresses, thecontinuously updated scout image is expected to become a progressivelymore accurate probability distribution. Accordingly, PET imaging dataacquired in any given interval [t_(o),t_(o)+Δt] (where Δt is arelatively short time interval but long enough to include astatistically significant count of e-h annihilation event detections)can be expected to conform with the probability distribution of thegenerated image with progressively greater accuracy as time t_(o)progresses forward through the PET imaging data acquisition.

If, however, this conformance with the generated image is observed toabruptly decrease at a certain value of t_(o), this may be indicative ofsubject motion (or perhaps some other problem, such as an equipmentfailure) at about the time of the abrupt decrease. Remedial action canthen be taken (for example, discarding data subsequent to the motion ifit occurs late enough in the acquisition session, or discarding theearlier data if the motion occurs early in the acquisition session andextending the time of acquisition accordingly). Moreover, for list modePET data in which the absolute time of each e-h annihilation event isstored, such analysis for detecting subject motion can be performedretrospectively on the stored PET imaging data set.

The illustrative processing components 16, 32, 36, 40, 52, 54, 60diagrammatically depicted in FIG. 1 as examples may be variouslyembodied by one or more digital processors. In some embodiments, theillustrated computer 18 is suitably programmed to define the digitalprocessor embodying one, some, or all of the illustrative processingcomponents 16, 32, 36, 40, 52, 54, 60. The scout navigator 36 issuitably embodied by the digital processor of the computer 18 along withthe display 20 for displaying the generated image 34 for user navigationpurposes. Additionally or alternatively, one, some, or all of theillustrative processing components 16, 32, 36, 40, 52, 54, 60 may beembodied by one or more digital processors (not shown) that arededicated to performing the disclosed operations implemented by theseillustrative processing components 16, 32, 36, 40, 52, 54, 60.

Still further, the disclosed operations implemented by one, some, or allof the illustrative processing components 16, 32, 36, 40, 52, 54, 60 maybe embodied by storage medium storing instructions executable by adigital processor (such as the illustrative computer 18) to perform thedisclosed operations. Such a storage medium may, for example, compriseone or more of the following storage media: a hard disk drive or othermagnetic storage medium or media; an optical disk or other opticalstorage medium or media; a random access memory (RAM), read-only memory(ROM), flash memory, or other electronic storage medium or media;various combinations thereof; or so forth.

This application has described one or more preferred embodiments.Modifications and alterations may occur to others upon reading andunderstanding the preceding detailed description. It is intended thatthe application be construed as including all such modifications andalterations insofar as they come within the scope of the appended claimsor the equivalents thereof.

Having thus described the preferred embodiments, the invention is now claimed to be:
 1. A method of processing a positron emission tomography (PET) imaging data set acquired of a subject, the method comprising: generating image content independently for each positron-electron annihilation event of a plurality of positron-electron annihilation events of the PET imaging data set based on time-of-flight (TOF) localization to form a generated image comprising an accumulation of the independently generated image content, wherein the generating comprises increasing values of image pixels or voxels of an image space by amounts corresponding to a TOF kernel representing the TOF localization such that the accumulation of value increases forms the generated image; wherein the generating operations are performed by a digital processor.
 2. The method as set forth in claim 1, further comprising: delineating a spatial contour of an image of the subject in the PET imaging data set based on the generated image.
 3. The method as set forth in claim 2, wherein the delineating comprises: applying an edge detector or image segmentation algorithm to the generated image.
 4. The method as set forth in claim 3, wherein the delineating further comprises: smoothing the generated image prior to applying the edge detector or image segmentation algorithm.
 5. The method as set forth in claim 2, further comprising: constructing a subject attenuation map for use in PET image reconstruction based in part on the spatial contour.
 6. A non-transitory storage medium storing instructions executable by a digital processor to perform a method comprising: generating image content independently for each positron-electron annihilation event of a plurality of positron-electron annihilation events of the PET imaging data set based on time-of-flight (TOF) localization to form a generated image comprising an accumulation of the independently generated image content; delineating a spatial contour of an image of the subject in the PET imaging data set based on the generated image; constructing a subject attenuation map for use in PET image reconstruction based in part on the spatial contour; and reconstructing the PET imaging data set to produce a reconstructed image using the constructed subject attenuation map.
 7. The non-transitory storage medium as set forth in claim 6, wherein the generating comprises: increasing a value of an image pixel or voxel of an image space at a peak of the TOF localization by a selected amount such that the accumulation of value increases forms the generated image.
 8. The non-transitory storage medium as set forth in claim 6, wherein the constructing comprises: spatially extending a truncated subject attenuation map based on the spatial contour to construct the subject attenuation map for use in PET image reconstruction.
 9. The non-transitory storage medium as set forth in claim 6, wherein the constructing comprises: spatially registering a subject attenuation map with the spatial contour to construct the subject attenuation map for use in PET image reconstruction.
 10. A method of processing a positron emission tomography (PET) imaging data set acquired of a subject, the method comprising: independently localizing each positron-electron annihilation event of the PET imaging data set based on time-of-flight (TOF) localization of the positron-electron annihilation event to form a generated image; and performing an iterative reconstruction of the PET imaging data set to produce a reconstructed image, the iterative reconstruction operating by starting with an initial image that is the generated image or that is derived from the generated image, simulating PET imaging data for the initial image, and iteratively adjusting the initial image and repeating the simulation to match the simulated PET imaging data with the PET imaging data set; wherein the independent localizing and iterative reconstruction operations are performed by a digital processor.
 11. The method as set forth in claim 10, wherein the independent localizing comprises: independently localizing each positron-electron annihilation event of the PET imaging data to a most probable voxel or pixel based on the time-of-flight (TOF) localization of the positron-electron annihilation event.
 12. The method as set forth in claim 10, wherein the independent localizing comprises: defining an intensity distribution contribution to the generated image corresponding to the time-of-flight (TOF) localization of the positron-electron annihilation event.
 13. The method as set forth in claim 10, further comprising: displaying the generated image.
 14. The method as set forth in claim 10, further comprising: delineating a spatial contour of an image of the subject in the PET imaging data set based on the generated image.
 15. The method as set forth in claim 14, further comprising: constructing a subject attenuation map for use in PET image reconstruction based at least in part on the spatial contour.
 16. A method of positron emission tomography (PET) imaging comprising: determining a likely location of a detected positron-electron annihilation event based on time-of-flight (TOF) information; repeating the determining for a plurality of detected positron-electron annihilation events to generate a scout image; and generating an attenuation map based on the scout image; wherein the determining, repeating, and generating operations are performed by a digital processor.
 17. The method as set forth in claim 16, further comprising: performing, by the digital processor, an iterative reconstruction of the plurality of detected positron-electron annihilation events using the scout image as an initial image to generate a reconstructed image.
 18. The PET imaging method as set forth in claim 16, further comprising: performing medical screening based on the scout image.
 19. The PET imaging method as set forth in claim 16, further comprising: determining a subject contour based on the scout image, the determining being performed by the digital processor.
 20. A method of positron emission tomography (PET) imaging comprising: determining a likely location of a detected positron-electron annihilation event based on time-of-flight (TOF) information; repeating the determining for a plurality of detected positron-electron annihilation events to generate a scout image; wherein the determining and repeating are performed during detection of the detected positron-electron annihilation events and the method further: comprises identifying subject motion based on inconsistency of more recently detected positron-electron annihilation events with the scout image; wherein the determining, repeating, and identifying operations are performed by a digital processor. 